How do I install the Funnel Analysis skill?

Run npx skillmds@latest add shyamw-cloud/funnel-analysis in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

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Which AI agents work with Funnel Analysis?

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Is Funnel Analysis free to use?

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Who published Funnel Analysis?

shyamw-cloud (@shyamw-cloud) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Funnel Analysis by shyamw-cloud — Agent Skill

Name: Funnel Analysis
Rating: 4.2 (5 reviews)
Author: shyamw-cloud

Funnel Analysis

Use to diagnose drop-off in an acquisition or activation funnel. Produces step-by-step conversion rates, identifies the single biggest leak, hypothesizes causes, and recommends one experiment to run. Always declares assumptions and data source.

shyamw-cloud Updated Sep 22, 2026

File contents

funnel-analysis

The point of a funnel is not to celebrate the top number — it's to find the single biggest leak and fix it.

Inputs to gather

The funnel definition — ordered list of events (e.g. visit → signup → activate → paid).
Window — the date range (typically last 30 days).
Segment — all users? a cohort? a channel?
Data source — PostHog, Mixpanel, Amplitude, GA4, custom SQL.

Method

Pull conversion at each step.
Compute step-to-step rates and the overall rate.
Identify the step with the lowest conversion rate, not the lowest absolute count.
Compare that step's rate to industry benchmark (if known) and to last period.
Hypothesize 3 causes. Rank them by likelihood × ease-to-test.
Recommend one experiment.

Output format

## Funnel
| Step | Users | Step-to-step | Cumulative |
|---|---|---|---|
| Visit | 12,400 | 100% | 100% |
| Signup | 1,488 | 12.0% | 12.0% |
| Activate | 446 | 30.0% | 3.6% |
| Paid | 67 | 15.0% | 0.54% |

Window: <date range>
Segment: <segment>
Source: <data source>

## Biggest leak
**Activate → Paid (15.0%)** is the lowest step rate. Industry benchmark for B2B SaaS at this stage is 20-30%. We are 5-15 points below.

## Hypotheses
1. **Pricing-page friction** (high likelihood, easy to test) — users land on pricing without seeing ROI proof.
2. **Onboarding incomplete** (medium, medium) — users hit the paywall before reaching their "aha moment."
3. **Wrong ICP traffic** (medium, hard) — top-of-funnel is bringing tire-kickers.

## Recommended experiment
A/B test: insert a one-screen ROI calculator between activate and pricing.
Hypothesis: lifts Activate → Paid from 15% → 20%.
Sample size needed: ~3,500 users per variant for 95% confidence at 5pp lift.
Expected runtime: 4 weeks at current traffic.

## Caveats
- "Activate" is currently defined as <event>; some teams define it differently.
- Last week of data may be incomplete due to <reason>.

Hard rules

Always declare the funnel definition explicitly. "Activated" means nothing without the event.
Never average step rates — multiply them for end-to-end conversion.
Don't recommend an experiment without a sample-size estimate.
If a step has < 200 users in the window, flag the rate as "low confidence."

---name: funnel-analysisdescription: Use to diagnose drop-off in an acquisition or activation funnel. Produces step-by-step conversion rates, identifies the single biggest leak, hypothesizes causes, and recommends one experiment to run. Always declares assumptions and data source.---# funnel-analysisThe point of a funnel is not to celebrate the top number — it's to find the **single biggest leak** and fix it.## Inputs to gather- **The funnel definition** — ordered list of events (e.g. visit → signup → activate → paid).- **Window** — the date range (typically last 30 days).- **Segment** — all users? a cohort? a channel?- **Data source** — PostHog, Mixpanel, Amplitude, GA4, custom SQL.## Method1. Pull conversion at each step.2. Compute step-to-step rates and the overall rate.3. Identify the step with the **lowest conversion rate**, not the lowest absolute count.4. Compare that step's rate to industry benchmark (if known) and to last period.5. Hypothesize 3 causes. Rank them by likelihood × ease-to-test.6. Recommend one experiment.## Output format```## Funnel| Step | Users | Step-to-step | Cumulative ||---|---|---|---|| Visit | 12,400 | 100% | 100% || Signup | 1,488 | 12.0% | 12.0% || Activate | 446 | 30.0% | 3.6% || Paid | 67 | 15.0% | 0.54% |Window: <date range>Segment: <segment>Source: <data source>## Biggest leak**Activate → Paid (15.0%)** is the lowest step rate. Industry benchmark for B2B SaaS at this stage is 20-30%. We are 5-15 points below.## Hypotheses1. **Pricing-page friction** (high likelihood, easy to test) — users land on pricing without seeing ROI proof.2. **Onboarding incomplete** (medium, medium) — users hit the paywall before reaching their "aha moment."3. **Wrong ICP traffic** (medium, hard) — top-of-funnel is bringing tire-kickers.## Recommended experimentA/B test: insert a one-screen ROI calculator between activate and pricing.Hypothesis: lifts Activate → Paid from 15% → 20%.Sample size needed: ~3,500 users per variant for 95% confidence at 5pp lift.Expected runtime: 4 weeks at current traffic.## Caveats- "Activate" is currently defined as <event>; some teams define it differently.- Last week of data may be incomplete due to <reason>.```## Hard rules- Always declare the funnel definition explicitly. "Activated" means nothing without the event.- Never average step rates — multiply them for end-to-end conversion.- Don't recommend an experiment without a sample-size estimate.- If a step has < 200 users in the window, flag the rate as "low confidence."

Frequently asked questions